Improve Energy Efficiency by Processor Overclocking and Memory Frequency Scaling

Wu, Feihao; Chen, Juan; Dong, Yong; Zheng, Wenxu; Pan, Xiaodong; Sun, Yuyang

doi:10.1109/hpcc/smartcity/dss.2018.00159

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…There are many ways to overclock a CPU [28][29][30][31][32]. Wu et al [29] utilized processor overclocking and memory-frequency scaling to achieve better performance and lower power consumption, bringing about better energy efficiencies for their benchmarks. Jang et al [30] adopted the F-overclocking technique, which increased clock frequency without changing supply voltage.…”

Section: Overclockingmentioning

confidence: 99%

Joint time delay and energy optimization with intelligent overclocking in edge computing

Wang

Xiong

Chen

et al. 2020

Sci. China Inf. Sci.

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Section: Overclockingmentioning

confidence: 99%

Joint time delay and energy optimization with intelligent overclocking in edge computing

Wang

Xiong

Chen

et al. 2020

Sci. China Inf. Sci.

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AOA: Adaptive Overclocking Algorithm on CPU-GPU Heterogeneous Platforms

Chen

Sun

et al. 2023

Algorithms and Architectures for Parallel Processing

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Although GPUs have been used to accelerate various convolutional neural network algorithms with good performance, the demand for performance improvement is still continuously increasing. CPU/GPU overclocking technology brings opportunities for further performance improvement in CPU-GPU heterogeneous platforms. However, CPU/GPU overclocking inevitably increases the power of the CPU/GPU, which is not conducive to energy conservation, energy efficiency optimization, or even system stability. How to effectively constrain the total energy to remain roughly unchanged during the CPU/GPU overclocking is a key issue in designing adaptive overclocking algorithms. There are two key factors during solving this key issue. Firstly, the dynamic power upper bound must be set to reflect the real-time behavior characteristics of the program so that algorithm can better meet the total energy unchanging constraints; secondly, instead of independently overclocking at both CPU and GPU sides, coordinately overclocking on CPU-GPU must be considered to adapt to real-time load balance for higher performance improvement and better energy constraints. This paper proposes an Adaptive Overclocking Algorithm (AOA) on CPU-GPU heterogeneous platforms to achieve the goal of performance improvement while the total energy remains roughly unchanged. AOA uses the function $$F_k$$ F k to describe the variable power upper bound and introduces the load imbalance factor W to realize the CPU-GPU coordinated overclocking. Through the verification of several types convolutional neural network algorithms on two CPU-GPU heterogeneous platforms (Intel$$^\circledR $$ ® Xeon E5-2660 & NVIDIA$$^\circledR $$ ® Tesla K80; Intel$$^\circledR $$ ® Core™i9-10920X & NIVIDIA$$^\circledR $$ ® GeForce RTX 2080Ti), AOA achieves an average of 10.7% performance improvement and 4.4% energy savings. To verify the effectiveness of the AOA, we compare AOA with other methods including automatic boost, the highest overclocking and static optimal overclocking.

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Analysis of Overclock Ram Galax Hall Of Fame For Daily Needs

Kharisma¹,

Putro²

2019

2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

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Improve Energy Efficiency by Processor Overclocking and Memory Frequency Scaling

Cited by 5 publications

References 24 publications

Joint time delay and energy optimization with intelligent overclocking in edge computing

Joint time delay and energy optimization with intelligent overclocking in edge computing

AOA: Adaptive Overclocking Algorithm on CPU-GPU Heterogeneous Platforms

Analysis of Overclock Ram Galax Hall Of Fame For Daily Needs

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